6 papers
Towards Robust Semantic Video Transmission over Block Erasure Channels
Nargis Fayaz, Homa Esfahanizadeh, Matin Mortaheb +2
This paper investigates semantic-aware neural joint source-channel coding (JSCC) for robust video transmission over block erasure channels. We propose a neural video compression fr…
Multi-Modal Semantic Communication
Matin Mortaheb, Erciyes Karakaya, Sennur Ulukus
Semantic communication aims to transmit information most relevant to a task rather than raw data, offering significant gains in communication efficiency for applications such as te…
Re-ranking the Context for Multimodal Retrieval Augmented Generation
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and re…
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particul…
Efficient Semantic Communication Through Transformer-Aided Compression
Matin Mortaheb, Mohammad A. Amir Khojastepour, Sennur Ulukus
Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to addre…
Age-: Communication-Efficient Federated Learning Using Age Factor
Matin Mortaheb, Priyanka Kaswan, Sennur Ulukus
Federated learning (FL) is a collaborative approach where multiple clients, coordinated by a parameter server (PS), train a unified machine-learning model. The approach, however, s…